Safe autonomous vehicles must be able to predict and react to the drivers around them. Previous control methods rely heavily on pre-computation and are unable to react to dynamic events as they unfold in real-time. In this paper, we extend Model Predictive Path Integral Control (MPPI) using differential game theory and introduce Best-Response MPPI (BR-MPPI) for real-time multi-vehicle interactions. Experimental results are presented using two AutoRally platforms in a racing format with BR-MPPI competing against a skilled human driver at the Georgia Tech Autonomous Racing Facility.
End2Race: An End-to-End Learning Framework for Multi-Vehicle Autonomous Racing
Department of Civil and Environmental Engineering, University of Michigan（土木与环境工程系，密歇根大学）；University of Michigan Transportation Research Institute（密歇根大学交通研究所）
This paper describes Ariel Team's autonomous racing controller for the Indy Autonomous Challenge (IAC) simulation race. IAC is the first multi-vehicle autonomous head-to-head competition, reaching speeds of 300 km/h along an oval track, modeled after the Indianapolis Motor Speedway (IMS). Our racing controller attempts to maximize progress along the track while avoiding collisions with opponent vehicles and obeying the race rules. To this end, the racing controller first computes a race line offline. Then, it repeatedly computes online a small set of dynamically feasible maneuver candidates, e…
Accelerating Autonomy: Insights from Pro Racers in the Era of Autonomous Racing - An Expert Interview Study
Technical University of Munich（慕尼黑工业大学）；TUM School of Engineering and Design（慕尼黑工业大学工程与设计学院）；Munich Institute of Robotics and Machine Intelligence (MIRMI)（慕尼黑机器人与机器智能研究所）
Head-to-Head autonomous racing at the limits of handling in the A2RL challenge
Institute of Automotive Technology, TUM（汽车技术研究所，慕尼黑技术大学）；Chair of Automatic Control, TUM（自动控制教授座，慕尼黑技术大学）；Professorship Autonomous Vehicle Systems, TUM（自主车辆系统教授职位，慕尼黑技术大学）
University of Pennsylvania（宾夕法尼亚大学）；ETH Zurich（苏黎世联邦理工学院）；California Institute of Technology（加州理工学院）；Technical University of Munich（慕尼黑工业大学）；University of Virginia（弗吉尼亚大学）；Clemson University（克莱姆森大学）
The rising popularity of self-driving cars has led to the emergence of a new research field in the recent years: Autonomous racing. Researchers are developing software and hardware for high performance race vehicles which aim to operate autonomously on the edge of the vehicles limits: High speeds, high accelerations, low reaction times, highly uncertain, dynamic and adversarial environments. This paper represents the first holistic survey that covers the research in the field of autonomous racing. We focus on the field of autonomous racecars only and display the algorithms, methods and approac…
For decades, motorsport has been an incubator for innovations in the automotive sector and brought forth systems like disk brakes or rearview mirrors. Autonomous racing series such as Roborace, F1Tenth, or the Indy Autonomous Challenge (IAC) are envisioned as playing a similar role within the autonomous vehicle sector, serving as a proving ground for new technology at the limits of the autonomous systems capabilities. This paper outlines the software stack and approach of the TUM Autonomous Motorsport team for their participation in the Indy Autonomous Challenge, which holds two competitions: …
(hu)Man vs. Machine: In the Future of Motorsport, can Autonomous Vehicles Compete?
Autonomous Driving and Intelligent Transport Group, Oxford Brookes University, UK（自主驾驶与智能交通组，奥克斯伯里大学，英国）；School of Engineering, Computing & Mathematics, Oxford Brookes University, UK（工程、计算与数学学院，奥克斯伯里大学，英国）；Artificial Intelligence, Data Analysis and Systems Institute, Oxford Brookes University, UK（人工智能、数据分析与系统研究所，奥克斯伯里大学，英国）
Longitudinal Control for Autonomous Racing with Combustion Engine Vehicles
Technical University of Munich, Germany（慕尼黑技术大学）；School of Engineering & Design（工程与设计学院）；Department of Engineering Physics and Computation（工程物理与计算系）；Institute of Automatic Control（自动控制研究所）；Department of Mobility Systems Engineering（移动系统工程系）；Institute of Automotive Technology（汽车技术研究所）
Iterative learning control has been successfully used for several decades to improve the performance of control systems that perform a single repeated task. Using information from prior control executions, learning controllers gradually determine open-loop control inputs whose reference tracking performance can exceed that of traditional feedback-feedforward control algorithms. This paper considers iterative learning control for a previously unexplored field: autonomous racing. Racecars are driven multiple laps around the same sequence of turns while operating near the physical limits of tire-…
The way to full autonomy of public road vehicles requires the step-by-step replacement of the human driver, with the ultimate goal of replacing the driver completely. Eventually, the driving software has to be able to handle all situations that occur on its own, even emergency situations. These particular situations require extreme combined braking and steering actions at the limits of handling to avoid an accident or to diminish its consequences. An average human driver is not trained to handle such extreme and rarely occurring situations and therefore often fails to do so. However, professio…
A2RL V\textsubscript{max}: The A2RL autonomous racing dataset for long-range, high-speed perception and multi-vehicle interaction
Institute of Measurement and Control Systems, Karlsruhe Institute of Technology（测量与控制系统研究所，卡尔斯鲁厄理工学院）；Institute of Automotive Technology, Technical University of Munich（汽车技术研究所，慕尼黑工业大学）；Professorship of Autonomous Vehicle Systems, Technical University of Munich（自动驾驶车辆系统教授职位，慕尼黑工业大学）；University of Modena and Reggio Emilia（摩德纳大学和雷焦艾米利亚大学）；Humda Lab, Széchenyi István University（胡姆达实验室，塞切尼·伊什特万大学）；Politecnico di Milano（米兰理工大学）；Constructor University（康斯坦丁大学）；Beijing Institute of Technology（北京理工大学）；Nanyang Technological University（南洋理工大学）；Code 19 Racing（代码19赛车）；Munich Institute of Robotics and Machine Intelligence (MIRMI), Technical University of Munich（慕尼黑机器人与机器智能研究所（MIRMI），慕尼黑工业大学）
BETTY Dataset: A Multi-modal Dataset for Full-Stack Autonomy
Robotics Institute, Carnegie Mellon University（卡内基梅隆大学机器人研究所）；University of Modena and Reggio Emilia（摩德纳和雷吉奥艾米利亚大学）；University of Waterloo（滑铁卢大学）；University of Pittsburgh（匹兹堡大学）
University of Zurich（苏黎世大学）；ETH Zurich（苏黎世联邦理工学院）；Intel（英特尔）
Autonomous micro aerial vehicles still struggle with fast and agile maneuvers, dynamic environments, imperfect sensing, and state estimation drift. Autonomous drone racing brings these challenges to the fore. Human pilots can fly a previously unseen track after a handful of practice runs. In contrast, state-of-the-art autonomous navigation algorithms require either a precise metric map of the environment or a large amount of training data collected in the track of interest. To bridge this gap, we propose an approach that can fly a new track in a previously unseen environment without a precise …
Institut de Robòtica i Informàtica Industrial (CSIC-UPC)（机器人与工业信息研究所（CSIC-UPC））；Universitat Politécnica de Catalunya（加泰罗尼亚理工大学）；Univ. Grenoble Alpes（格勒诺布尔阿尔卑斯大学）；CNRS（法国国家科学研究中心）；Grenoble INP（格勒诺布尔国立高等物理、数学与信息学院）
In this paper, the Model Predictive Control (MPC) and Moving Horizon Estimator (MHE) strategies using a data-driven approach to learn a Takagi-Sugeno (TS) representation of the vehicle dynamics are proposed to solve autonomous driving control problems in real-time. To address the TS modeling, we use the Adaptive Neuro-Fuzzy Inference System (ANFIS) approach to obtain a set of polytopic-based linear representations as well as a set of membership functions relating in a non-linear way the different linear subsystems. The proposed control approach is provided by racing-based references of an exte…
High-Fidelity Multi-Body Simulator for Autonomous Racing
University of Modena and Reggio Emilia（摩德纳与雷焦艾米利亚大学）；Megaride S.r.l.（Megaride 有限责任公司）；University of Naples Federico II（那不勒斯费德里科二世大学）；Claytex（Claytex 公司）
Existing research on autonomous driving primarily focuses on urban driving, which is insufficient for characterising the complex driving behaviour underlying high-speed racing. At the same time, existing racing simulation frameworks struggle in capturing realism, with respect to visual rendering, vehicular dynamics, and task objectives, inhibiting the transfer of learning agents to real-world contexts. We introduce a new environment, where agents Learn-to-Race (L2R) in simulated competition-style racing, using multimodal information--from virtual cameras to a comprehensive array of inertial me…
FlightGoggles is a photorealistic sensor simulator for perception-driven robotic vehicles. The key contributions of FlightGoggles are twofold. First, FlightGoggles provides photorealistic exteroceptive sensor simulation using graphics assets generated with photogrammetry. Second, it provides the ability to combine (i) synthetic exteroceptive measurements generated in silico in real time and (ii) vehicle dynamics and proprioceptive measurements generated in motio by vehicle(s) in a motion-capture facility. FlightGoggles is capable of simulating a virtual-reality environment around autonomous ve…
Technical University of Munich（慕尼黑工业大学）；University of Pennsylvania（宾夕法尼亚大学）
Motorsport has always been an enabler for technological advancement, and the same applies to the autonomous driving industry. The team TUM Auton-omous Motorsports will participate in the Indy Autonomous Challenge in Octo-ber 2021 to benchmark its self-driving software-stack by racing one out of ten autonomous Dallara AV-21 racecars at the Indianapolis Motor Speedway. The first part of this paper explains the reasons for entering an autonomous vehicle race from an academic perspective: It allows focusing on several edge cases en-countered by autonomous vehicles, such as challenging evasion mane…
While the majority of autonomous driving research has concentrated on everyday driving scenarios, further safety and performance improvements of autonomous vehicles require a focus on extreme driving conditions. In this context, autonomous racing is a new area of research that has been attracting considerable interest recently. Due to the fact that a vehicle is driven by its perception, planning, and control limits during racing, numerous research and development issues arise. This paper provides a comprehensive overview of the autonomous racing system built by team KAIST for the Indy Autonomo…
King Abdullah University of Science and Technology（阿卜杜拉国王科技大学）
Autonomous UAV racing has recently emerged as an interesting research problem. The dream is to beat humans in this new fast-paced sport. A common approach is to learn an end-to-end policy that directly predicts controls from raw images by imitating an expert. However, such a policy is limited by the expert it imitates and scaling to other environments and vehicle dynamics is difficult. One approach to overcome the drawbacks of an end-to-end policy is to train a network only on the perception task and handle control with a PID or MPC controller. However, a single controller must be extensively …
The development of software components for autonomous driving functions should always include an extensive and rigorous evaluation. Since real-world testing is expensive and safety-critical -- especially when facing dynamic racing scenarios at the limit of handling -- a favored approach is simulation-based testing. In this work, we propose an open-source graphical user interface, which allows the generation of a multi-vehicle scenario in a regular or even a race environment. The underlying method and implementation is elaborated in detail. Furthermore, we showcase the potential use-cases for t…
Carnegie Mellon University（卡内基梅隆大学）；Bosch Research Pittsburgh（博世匹兹堡研究院）；NVIDIA（英伟达）；AICrowd；Amazon（亚马逊）；Arrival Ltd.（Arrival公司）；Pinterest
We present the results of our autonomous racing virtual challenge, based on the newly-released Learn-to-Race (L2R) simulation framework, which seeks to encourage interdisciplinary research in autonomous driving and to help advance the state of the art on a realistic benchmark. Analogous to racing being used to test cutting-edge vehicles, we envision autonomous racing to serve as a particularly challenging proving ground for autonomous agents as: (i) they need to make sub-second, safety-critical decisions in a complex, fast-changing environment; and (ii) both perception and control must be robu…
Cooperatively planning for multiple agents has been proposed as a promising method for strategic and motion planning for automated vehicles. By taking into account the intent of every agent, the ego agent can incorporate future interactions with human-driven vehicles into its planning. The problem is often formulated as a multi-agent game and solved using iterative algorithms operating on a discretized action or state space. Even if converging to a Nash equilibrium, the result will often be only sub-optimal. In this paper, we define a linear differential game for a set of interacting agents an…
Autonomous vehicles rely heavily upon their perception subsystems to see the environment in which they operate. Unfortunately, the effect of variable weather conditions presents a significant challenge to object detection algorithms, and thus it is imperative to test the vehicle extensively in all conditions which it may experience. However, development of robust autonomous vehicle subsystems requires repeatable, controlled testing - while real weather is unpredictable and cannot be scheduled. Real-world testing in adverse conditions is an expensive and time-consuming task, often requiring acc…
Indian Institute of Technology, Kharagpur（印度理工学院卡拉格普尔分校）；IUPUI（印第安纳大学-普渡大学印第安纳波利斯联合分校）
The paper presents a strategy for the control of anautonomous racing car on a pre-mapped track. Using a dynamic model of the vehicle, the optimal racing line is computed, taking track boundaries into account. With the optimal racing line as areference, a local nonlinear model predictive controller (NMPC) is proposed, which takes into account multiple local objectives like making more progress along the race line, avoiding collision with opponent vehicles, and use of drafting to achieve more progress.
A good racing strategy and in particular the racing line is decisive to winning races in Formula 1, MotoGP, and other forms of motor racing. The racing line defines the path followed around a track as well as the optimal speed profile along the path. The objective is to minimize lap time by driving the vehicle at the limits of friction and handling capability. The solution naturally depends upon the geometry of the track and vehicle dynamics. We introduce a novel method to compute the racing line using Bayesian optimization. Our approach is fully data-driven and computationally more efficient …
University of Sannio（萨尼奥大学）；Czech Technical University in Prague（捷克布拉格技术大学）；Universitat Politècnica de Catalunya（加泰罗尼亚理工大学）
A Nonlinear Model Predictive Control (NMPC) strategy aimed at controlling a small-scale car model for autonomous racing competitions is presented in this paper. The proposed control strategy is concerned with minimizing the lap time while keeping the vehicle within track boundaries. The optimization problem considers both the vehicle's actuation limits and the lateral and longitudinal forces acting on the car modeled through the Pacejka's magic formula and a simple drivetrain model. Furthermore, the approach allows to safely race on a track populated by static obstacles generating collision-fr…
Georgia Institute of Technology（佐治亚理工学院）；Twentieth Century Fox（二十世纪福克斯）；Starfleet Academy（星际舰队学院）；Tyrell Inc.（泰瑞尔公司）
A fundamental aspect of racing is overtaking other race cars. Whereas previous research on autonomous racing has majorly focused on lap-time optimization, here, we propose a method to plan overtaking maneuvers in autonomous racing. A Gaussian process is used to learn the behavior of the leading vehicle. Based on the outputs of the Gaussian process, a stochastic Model Predictive Control algorithm plans optimistic trajectories, such that the controlled autonomous race car is able to overtake the leading vehicle. The proposed method is tested in a simple simulation scenario.
Korea Advanced Institute of Science and Technology（韩国科学技术院）
Resolving edge-cases in autonomous driving, head-to-head autonomous racing is getting a lot of attention from the industry and academia. In this study, we propose a game-theoretic model predictive control (MPC) approach for head-to-head autonomous racing and data-driven model identification method. For the practical estimation of nonlinear model parameters, we adopted the hyperband algorithm, which is used for neural model training in machine learning. The proposed controller comprises three modules: 1) game-based opponents' trajectory predictor, 2) high-level race strategy planner, and 3) MPC…
Carnegie Mellon University（卡内基梅隆大学）；Cleveland State University（克利夫兰州立大学）
Autonomous car racing is a challenging task, as it requires precise applications of control while the vehicle is operating at cornering speeds. Traditional autonomous pipelines require accurate pre-mapping, localization, and planning which make the task computationally expensive and environment-dependent. Recent works propose use of imitation and reinforcement learning to train end-to-end deep neural networks and have shown promising results for high-speed racing. However, the end-to-end models may be dangerous to be deployed on real systems, as the neural networks are treated as black-box mod…
Uncertainty-Aware Autonomous Vehicles: Predicting the Road Ahead
School of Engineering, Computing and Mathematics, Oxford Brookes University, UK（工程、计算与数学学院，奥克斯福德布鲁克斯大学，英国）；Autonomous Driving and Intelligent Transport, Oxford Brookes University, UK（自动驾驶与智能交通，奥克斯福德布鲁克斯大学，英国）；Cognitive Robotics, TU Delft, Netherlands（认知机器人学，代尔夫特理工大学，荷兰）
